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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.execution.datasources
import scala.collection.mutable.ArrayBuffer
import org.apache.hadoop.fs.Path
import org.apache.spark.internal.Logging
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.expressions
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.execution.{DataSourceScan, SparkPlan}
import org.apache.spark.sql.sources._
/**
* A strategy for planning scans over collections of files that might be partitioned or bucketed
* by user specified columns.
*
* At a high level planning occurs in several phases:
* - Split filters by when they need to be evaluated.
* - Prune the schema of the data requested based on any projections present. Today this pruning
* is only done on top level columns, but formats should support pruning of nested columns as
* well.
* - Construct a reader function by passing filters and the schema into the FileFormat.
* - Using an partition pruning predicates, enumerate the list of files that should be read.
* - Split the files into tasks and construct a FileScanRDD.
* - Add any projection or filters that must be evaluated after the scan.
*
* Files are assigned into tasks using the following algorithm:
* - If the table is bucketed, group files by bucket id into the correct number of partitions.
* - If the table is not bucketed or bucketing is turned off:
* - If any file is larger than the threshold, split it into pieces based on that threshold
* - Sort the files by decreasing file size.
* - Assign the ordered files to buckets using the following algorithm. If the current partition
* is under the threshold with the addition of the next file, add it. If not, open a new bucket
* and add it. Proceed to the next file.
*/
private[sql] object FileSourceStrategy extends Strategy with Logging {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case PhysicalOperation(projects, filters, l @ LogicalRelation(files: HadoopFsRelation, _, _))
if (files.fileFormat.toString == "TestFileFormat" ||
files.fileFormat.isInstanceOf[parquet.DefaultSource] ||
files.fileFormat.toString == "ORC") &&
files.sqlContext.conf.parquetFileScan =>
// Filters on this relation fall into four categories based on where we can use them to avoid
// reading unneeded data:
// - partition keys only - used to prune directories to read
// - bucket keys only - optionally used to prune files to read
// - keys stored in the data only - optionally used to skip groups of data in files
// - filters that need to be evaluated again after the scan
val filterSet = ExpressionSet(filters)
val partitionColumns =
l.resolve(files.partitionSchema, files.sqlContext.sessionState.analyzer.resolver)
val partitionSet = AttributeSet(partitionColumns)
val partitionKeyFilters =
ExpressionSet(filters.filter(_.references.subsetOf(partitionSet)))
logInfo(s"Pruning directories with: ${partitionKeyFilters.mkString(",")}")
val dataColumns =
l.resolve(files.dataSchema, files.sqlContext.sessionState.analyzer.resolver)
val bucketColumns =
AttributeSet(
files.bucketSpec
.map(_.bucketColumnNames)
.getOrElse(Nil)
.map(l.resolveQuoted(_, files.sqlContext.conf.resolver)
.getOrElse(sys.error(""))))
// Partition keys are not available in the statistics of the files.
val dataFilters = filters.filter(_.references.intersect(partitionSet).isEmpty)
// Predicates with both partition keys and attributes need to be evaluated after the scan.
val afterScanFilters = filterSet -- partitionKeyFilters
logInfo(s"Post-Scan Filters: ${afterScanFilters.mkString(",")}")
val selectedPartitions = files.location.listFiles(partitionKeyFilters.toSeq)
val filterAttributes = AttributeSet(afterScanFilters)
val requiredExpressions: Seq[NamedExpression] = filterAttributes.toSeq ++ projects
val requiredAttributes = AttributeSet(requiredExpressions)
val readDataColumns =
dataColumns
.filter(requiredAttributes.contains)
.filterNot(partitionColumns.contains)
val prunedDataSchema = readDataColumns.toStructType
logInfo(s"Pruned Data Schema: ${prunedDataSchema.simpleString(5)}")
val pushedDownFilters = dataFilters.flatMap(DataSourceStrategy.translateFilter)
logInfo(s"Pushed Filters: ${pushedDownFilters.mkString(",")}")
val readFile = files.fileFormat.buildReader(
sqlContext = files.sqlContext,
partitionSchema = files.partitionSchema,
dataSchema = prunedDataSchema,
filters = pushedDownFilters,
options = files.options)
val plannedPartitions = files.bucketSpec match {
case Some(bucketing) if files.sqlContext.conf.bucketingEnabled =>
logInfo(s"Planning with ${bucketing.numBuckets} buckets")
val bucketed =
selectedPartitions.flatMap { p =>
p.files.map(f => PartitionedFile(p.values, f.getPath.toUri.toString, 0, f.getLen))
}.groupBy { f =>
BucketingUtils
.getBucketId(new Path(f.filePath).getName)
.getOrElse(sys.error(s"Invalid bucket file ${f.filePath}"))
}
(0 until bucketing.numBuckets).map { bucketId =>
FilePartition(bucketId, bucketed.getOrElse(bucketId, Nil))
}
case _ =>
val maxSplitBytes = files.sqlContext.conf.filesMaxPartitionBytes
logInfo(s"Planning scan with bin packing, max size: $maxSplitBytes bytes")
val splitFiles = selectedPartitions.flatMap { partition =>
partition.files.flatMap { file =>
assert(file.getLen != 0, file.toString)
(0L to file.getLen by maxSplitBytes).map { offset =>
val remaining = file.getLen - offset
val size = if (remaining > maxSplitBytes) maxSplitBytes else remaining
PartitionedFile(partition.values, file.getPath.toUri.toString, offset, size)
}
}
}.toArray.sortBy(_.length)(implicitly[Ordering[Long]].reverse)
val partitions = new ArrayBuffer[FilePartition]
val currentFiles = new ArrayBuffer[PartitionedFile]
var currentSize = 0L
/** Add the given file to the current partition. */
def addFile(file: PartitionedFile): Unit = {
currentSize += file.length
currentFiles.append(file)
}
/** Close the current partition and move to the next. */
def closePartition(): Unit = {
if (currentFiles.nonEmpty) {
val newPartition =
FilePartition(
partitions.size,
currentFiles.toArray.toSeq) // Copy to a new Array.
partitions.append(newPartition)
}
currentFiles.clear()
currentSize = 0
}
// Assign files to partitions using "First Fit Decreasing" (FFD)
// TODO: consider adding a slop factor here?
splitFiles.foreach { file =>
if (currentSize + file.length > maxSplitBytes) {
closePartition()
addFile(file)
} else {
addFile(file)
}
}
closePartition()
partitions
}
val scan =
DataSourceScan(
readDataColumns ++ partitionColumns,
new FileScanRDD(
files.sqlContext,
readFile,
plannedPartitions),
files,
Map(
"Format" -> files.fileFormat.toString,
"PushedFilters" -> pushedDownFilters.mkString("[", ", ", "]"),
"ReadSchema" -> prunedDataSchema.simpleString))
val afterScanFilter = afterScanFilters.toSeq.reduceOption(expressions.And)
val withFilter = afterScanFilter.map(execution.Filter(_, scan)).getOrElse(scan)
val withProjections = if (projects == withFilter.output) {
withFilter
} else {
execution.Project(projects, withFilter)
}
withProjections :: Nil
case _ => Nil
}
}
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